Filament tracking and encoding for complex biological networks

We present a framework for segmenting and storing filament networks from scalar volume data. Filament structures are commonly found in data generated using high-throughput microscopy. These data sets can be several gigabytes in size because they are either spatially large or have a high number of scalar channels. Filaments in microscopy data sets are difficult to segment because their diameter is often near the sampling resolution of the microscope, yet single filaments can span large data sets. We describe a novel method to trace filaments through scalar volume data sets that is robust to both noisy and under-sampled data. We use a GPU-based scheme to accelerate the tracing algorithm, making it more useful for large data sets. After the initial structure is traced, we can use this information to create a bounding volume around the network and encode the volumetric data associated with it. Taken together, this framework provides a convenient method for accessing network structure and connectivity while providing compressed access to the original volumetric data associated with the network.